{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 引入t检验相关函数\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "# 引入Z检验相关函数\n",
    "from statsmodels.stats.weightstats import ztest\n",
    "\n",
    "# 引入绘图相关库\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## t检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>身高</th>\n",
       "      <th>地区</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>167</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>172</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>178</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>180</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>182</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>183</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>185</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>188</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>155</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>158</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>160</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>162</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>165</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>168</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>172</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>176</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>179</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>182</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     身高 地区\n",
       "0   165  A\n",
       "1   167  A\n",
       "2   172  A\n",
       "3   176  A\n",
       "4   178  A\n",
       "5   180  A\n",
       "6   182  A\n",
       "7   183  A\n",
       "8   185  A\n",
       "9   188  A\n",
       "10  155  B\n",
       "11  158  B\n",
       "12  160  B\n",
       "13  162  B\n",
       "14  165  B\n",
       "15  168  B\n",
       "16  172  B\n",
       "17  176  B\n",
       "18  179  B\n",
       "19  182  B"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height_df = pd.read_csv(\"height.csv\")\n",
    "height_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "region_a_height = height_df.query(\"地区 == 'A'\")['身高']\n",
    "region_b_height = height_df.query(\"地区 == 'B'\")['身高']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    165\n",
       "1    167\n",
       "2    172\n",
       "3    176\n",
       "4    178\n",
       "5    180\n",
       "6    182\n",
       "7    183\n",
       "8    185\n",
       "9    188\n",
       "Name: 身高, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_a_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    155\n",
       "11    158\n",
       "12    160\n",
       "13    162\n",
       "14    165\n",
       "15    168\n",
       "16    172\n",
       "17    176\n",
       "18    179\n",
       "19    182\n",
       "Name: 身高, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_b_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 替换图表的字体（你的系统上不一定有这个Heiti TC字体，如果没有的话需要替换成其它的）\n",
    "matplotlib.rc(\"font\",family='Heiti TC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 36523 (\\N{CJK UNIFIED IDEOGRAPH-8EAB}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 39640 (\\N{CJK UNIFIED IDEOGRAPH-9AD8}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n",
      "findfont: Font family 'Heiti TC' not found.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(region_a_height)\n",
    "sns.kdeplot(region_b_height)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.608375959216796 0.017783305969556976\n"
     ]
    }
   ],
   "source": [
    "# 进行t检验\n",
    "t_stat, p_value = ttest_ind(region_a_height, region_b_height)\n",
    "print(t_stat, p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "两组数据有显著差异\n"
     ]
    }
   ],
   "source": [
    "# 显著水平为0.05（显著水平一般用alpha字母表示）\n",
    "alpha = 0.5\n",
    "\n",
    "\n",
    "# 比较计算出的p值和显著水平，打印是否有显著差异的结论\n",
    "if p_value < alpha:\n",
    "    print('两组数据有显著差异')\n",
    "else:\n",
    "    print('两组数据无显著差异')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Z检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>身高</th>\n",
       "      <th>地区</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>175</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>169</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>185</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>173</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>164</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>163</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>183</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>189</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     身高 地区\n",
       "0   175  A\n",
       "1   169  A\n",
       "2   176  A\n",
       "3   185  A\n",
       "4   168  A\n",
       "..  ... ..\n",
       "61  173  B\n",
       "62  164  B\n",
       "63  163  B\n",
       "64  183  B\n",
       "65  189  B\n",
       "\n",
       "[66 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height_df2 = pd.read_csv(\"height2.csv\")\n",
    "height_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "region_a_height2 = height_df2.query(\"地区 == 'A'\")['身高']\n",
    "region_b_height2 = height_df2.query(\"地区 == 'B'\")['身高']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family 'Heiti TC' not found.\n",
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      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 36523 (\\N{CJK UNIFIED IDEOGRAPH-8EAB}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 39640 (\\N{CJK UNIFIED IDEOGRAPH-9AD8}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Font family 'Heiti TC' not found.\n",
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    {
     "data": {
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J0XDViwRpzpw5hIWF4erqSlRUFFu3br1q+SVLlhAeHo6rqyvdunVj5cqVVV5//fXXCQ8Px93dnaZNmxITE8OWLVuqlAkLC8NgMFR5vP3221b/bELYm9/2ZAAwuL1/o18c8kpu6hyEg9GgJovSzSZEg6R7grR48WKmTp3KjBkzSEpKIiIighEjRpCZmVlt+U2bNjFu3DgmT57Mjh07GD16NKNHj2bPnj2WMh06dGD27Nns3r2bjRs3EhYWxvDhwzl79myVa73xxhukpaVZHk8//bSmn1UIexBrx91rlXzcnOnXRp3N9vs+2ZtNiIbIoCiKomcAUVFR9OnTh9mzZwNgNpsJDQ3l6aef5tVXX72s/NixYyksLGTFihWWc/369SMyMpK5c+dWW0deXh7e3t6sWrWKYcOGAWoL0nPPPcdzzz1Xp7grr5mbm4uXl1edriFEY5N6rojB767BwWhg+//F0NTdWe+QdPNNwgmm/7SXXq2a8r8n++sdjhCiQk1/f+vaglRaWkpiYiIxMTGWc0ajkZiYGBISEqp9T0JCQpXyACNGjLhi+dLSUubNm4e3tzcRERFVXnv77bfx8/OjR48evPvuu5SXl18x1pKSEvLy8qo8hBBVVQ7Ojmrta9fJEajdbKCuKJ6ZL3uzCdHQ6JogZWVlYTKZCAqqust3UFAQ6enVN0unp6fXqPyKFSvw8PDA1dWVDz74gLi4OPz9/S2vP/PMMyxatIg1a9bw+OOP889//pOXX375irHOnDkTb29vyyM0NLS2H1eIRq8yQRrZ1X671yo1825CRAtvFAVW7at+yIAQov7SfQySVm644QaSk5PZtGkTI0eO5N57760yrmnq1KkMHTqU7t2788QTTzBr1iw++eQTSkpKqr3etGnTyM3NtTxOnjxpq48iRIOQmV9MYup5AIZ3lgQJYHjFOKzKxFEI0XDomiD5+/vj4OBARkZGlfMZGRkEB1f/AzY4OLhG5d3d3WnXrh39+vXjiy++wNHRkS+++OKKsURFRVFeXs6JEyeqfd3FxQUvL68qDyHERXH7MlAUiAz1IdjbVe9w6oXKgeqbjmaRV1ymczRCiNrQdQ6us7MzvXr1Ij4+ntGjRwPqIO34+HimTJlS7Xuio6OJj4+vMrg6Li6O6Ojoq9ZlNpuv2DoEkJycjNFoJDAwsNafQwgBsXtk9tql2gV60CbAnWNnC1l78Cy3R1xlX7qSfMg6DLknIeek+px3GopzoTgPSvLUMooCBuPFh7M7uPmpD3c/8GwG/h0goCP4tQcnSVaFqAvdFymZOnUqEyZMoHfv3vTt25cPP/yQwsJCJk2aBMD48eNp3rw5M2fOBODZZ59lyJAhzJo1i1tuuYVFixaxfft25s2bB0BhYSFvvfUWt99+O82aNSMrK4s5c+Zw+vRp7rnnHkAd6L1lyxZuuOEGPD09SUhI4Pnnn+fBBx+kadOm+twIIRqw3AtlJBw9B8CILkHXKG1fRnQJ5tO1R/ltb/rFBKkkH1K3wJkkSN+tPs4fr3sl5w5f4QUD+LWFVgOg9WD14SF/BApRE7onSGPHjuXs2bNMnz6d9PR0IiMjiY2NtQzETk1NxWi82BPYv39/Fi5cyN/+9jf++te/0r59e5YtW0bXrl0BcHBw4MCBA8yfP5+srCz8/Pzo06cPGzZsoEuXLoDaXbZo0SJef/11SkpKaN26Nc8//zxTp061/Q0QohFYezCTcrNC+0AP2gR46B1OvTKiSzCfrz3IhQPxlP++HMfUjXA6CRTT5YXdA6FpK/AOBZ9Q8GoBTXzAxQtcvcDFEwwOoJgBBcwmKC2AonPqo/Ac5KRA1iE4e0BtfTp3RH0kzVfrCOgEnW6FrmMgsJMtb4UQDYru6yA1VLIOkhAXPf3dDn7eeYYnh7bllZHheodTP5SXwLF1KPuWkZf8E94UVH3dpxW07AfB3SG4KwR1U7vIrEVRoCATzuyAExvg+Dq1perPQnpA74eh693g7Ga9uoWox2r6+1v3FiQhRMNWZjKz9qA6QzSmk3TfkLEPEr+CnYuhJBcD4A1kKV6kNO1PryG3Q+tB4NNS2zgMBvAMgo4j1QdAUTYciYe9S+FwnJo8LX8aVr0OUU9C30egiQwzEAIkQRJCXKdtJ7LJLy7H192ZyFA7/eVaXgr7foLtX0Dqnxat9QiGTrex23soo1eY8clvwtaIGByMBn3idPOF7veoj8IsSF4A2/4DOamw5h+QMBsGvwR9HwVHF31iFKKekARJCHFd4verrUc3dAzU7xe/XspLYMe3sPEDddYZqGOEwkepXVeth4LRSLjJjEf8Ks4VlpKYcp6+rX31jFrl7g8DnoV+T8HeH2HDe+q4pd//T030bv0A2gzVO0ohdCMJkhCizhRFIX6/ui6ZXXWvlZdA0jdqYpR3Wj3nEQS9J0PP8eDVrEpxJwcjN4YH8uOO08QfyKgfCVIlB0e1RanrXZC8EFb/A7KPwTd3QMT9cPM76gBxIexMo11JWwihvWNZhZw4V4Szg5FBHQL0Dkd7igIHfoHZfWDli2py5NkMbv4XPLsThr5yWXJU6cZwNYFcvb+ebjtidICeD8GUbdD3McAAOxfC3IFwcqve0Qlhc5IgCSHqrLL1KKqNLx4ujbxBOuswfDsGFt2vTqX3bAaj3oNnkiHqcXBqctW3D+4QgIPRwOHMAk5mF9km5rpw9YJR78LDsepA8pwU+Opm2Pq5miAKYSckQRJC1Nmq/ZWz1xrx4pBlxeosr39Hw9F4cHCGgVNhynZ1MHMNV6r2buJE71bqIPbVB+ppK9KftewHT2xU10syl6stZj8/AybZMkXYB0mQhBB1klOkDjgGGNZYxx+l7YR5Q9WxRuYyaD8C/rIZYmaAS+0XxKy8T/ENIUECcPWGMV/ATW+q25okfQPf3QelhXpHJoTmJEESQtTJ2oNnMZkVwoM9adG0kS0yaCqH9e/B58Pg7H5wD4CxC+CB79WtO+qochzS5qPnKCwpt1a02jIYYMAzMG4RODaBI6tg/u1w4bzekQmhKUmQhBB1sqpi/FHlL/1GI+ekOuZm9Ztqq1H4rWqrUadbr/vSbQM8aOnrRqnJzB9HsqwQrA11GAETloOrD5zeDv+9U93KRIhGShIkIUStlZnMrDt0FoBhjWn80bF1MG8InNqq7n9252cw9lt1zSArMBgMF2ezNZRutj8L7QuTVkITX3UV7m/HqBvvCtEISYIkhKi1bcfV1bP93J2JDPXRO5zrpyjwx8fw39Hqpq/NItQByhH3qV1MVvTnBKlBboUZ1AXG/6S2JJ3aBt+Pl4HbolGSBEkIUWuVs9duCG8Eq2eXFsKSiRD3GihmiHwAHv4NmrbSpLqoNr64OTuQmV/C3jN5mtShuWbd4aGl4OQGR1fDz8/JEgCi0ZEESQhRK4qiEH+gkayeXXAWvr4V9i0DoxPcMgvumHPNNY2uh4ujA4Paq1128fV10ciaaN4L7v5Knd2W/C1smKV3REJYlSRIQohaOXq2gJTK1bPbN+DVs7OPwZfD4UySOqZm4gro84jVu9Sqc7GbLUPzujTVcaS6WCaoW5QcjtM3HiGsSBIkIUStVHav9Wvrh3tDXT37dBL85yY1SfJpBZPj1IURbeSGjmqCtPNULmfzS2xWryb6TIZeEwEF/jdZvadCNAKSIAkhaqXBb057JF7tVivKUgdjT44D/3Y2DSHQy5XuLbwBWHOwAXezVbr5X9Cijzrtf8kkKC/VOyIhrpskSEKIGjtfeHH17Aa5/tHhOPhuHJQVQtsbYeIv4KnPMgWVrUj1dvPa2nB0gXvmQ5OmkJYMa/6hd0RCXDdJkIQQNbbmYCZmhYa5evah39WNZk0l6uKP4xaDi6du4VRuO7LxSBZlJrNucViNd3O4fbZ6/MdHcHSNvvEIcZ0kQRJC1FjlHmINbnPaQ7/B4gfAVAqdboN7vgZHZ11D6hrijZ+7MwUl5SSlNJJtOzrdCr0fVo9/egqKG+gyBkIgCZIQooZKy82sP6iunn1jQxp/dOg3WPxgRXJ0uzo13cFJ76gwGg0M7qDOAqxclbxRGP4WNG0NeafVtaWEaKAkQRJC1Mi2E9nkl5Tj7+FMZAsfvcOpmRN/wOKH1OSo82i4+8t6kRxVGtxBXQ+pUSVIzm5wR0VXW+LXcGytntEIUWeSIAkhaqRyc9obOgZibAirZ6fvUQdkm0qg4ygY8596lRwBlnWk9p7JIzO/WOdorChsoLqmFMDPz0JZI/pswm5IgiSEuCZFUSyrPjeIzWnPn4Bv74KSXGjZv961HFXy93ChW3N1uv+GQ1k6R2NlMa+DZ4j6/+KPj/SORohakwRJCHFNRzILSM2uXD3bOjvba6bgLPz3TijIgMAuMO47TbcOuV5DGuM4JFBnCI6omO6/8X01URKiAZEESQhxTZWrZ0fX99WzSwthwd0VK2S3VDdUbeKjd1RXNaSjmiBtOHwWk7mRbfja5S5oPRjKiyF2mt7RCFErkiAJIa6pQayebTbDj4+rCxW6+cFDy8AzWO+orqlHqA+ero6cLypj9+lcvcOxLoNB3avN6AgHV8KxdXpHJESNSYIkhLiq7MJSklIrVs+uz+OP1v4T9v8MDs5w33fg11bviGrE0cHIwHYVs9kONrJuNoCAjhfXRvr9b2oiK0QDIAmSEOKq1hxQV8/u1MyL5j71dCzP7h9g/bvq8W0fQ8sofeOppYvjkBrBtiPVGfIKuHhB+i7YvUTvaISoEUmQhBBXFX+gnnevnUqEZX9Rjwc8C5Hj9I2nDioXjEw+mUNOUSPc6NXdHwY+rx7HvyHT/kWDIAmSEOKKSsvNrK+Yfl4vp/fnpV3cX63DzTBsht4R1UmITxM6BHlgVtS92Rqlfk+CV3PIOwVJ8/WORohrqhcJ0pw5cwgLC8PV1ZWoqCi2bt161fJLliwhPDwcV1dXunXrxsqVK6u8/vrrrxMeHo67uztNmzYlJiaGLVu2VCmTnZ3NAw88gJeXFz4+PkyePJmCggKrfzYhGrItx89RUFKOv4cL3SvW66k3TOXww8NQkA6BnWHM52B00DuqOrN0szXGcUigLrUw6AX1eMMsKC3SNx4hrkH3BGnx4sVMnTqVGTNmkJSUREREBCNGjCAzs/q++E2bNjFu3DgmT57Mjh07GD16NKNHj2bPnj2WMh06dGD27Nns3r2bjRs3EhYWxvDhwzl79uIPngceeIC9e/cSFxfHihUrWL9+PY899pjmn1eIhqRyccgbwwPq3+rZq9+A1E3g7Aljv1XX3WnAKrvZ1h8+i6I0sun+lXo8pC6/UJAB27/QOxohrsqg6PwvMSoqij59+jB7trp3j9lsJjQ0lKeffppXX331svJjx46lsLCQFStWWM7169ePyMhI5s6dW20deXl5eHt7s2rVKoYNG8b+/fvp3Lkz27Zto3fv3gDExsYyatQoTp06RUhIyGXXKCkpoaSkpMo1Q0NDyc3NxcvL67rugRD1kaIoDH53DSezL/DZQ70Y0aUeTZk/sBIWVYw1uvcb6HyHvvFYQXGZicg3fqe4zEzsc4MID26kP1eS/gvLp6hLMTy7C1w89I5I2JnKnOBav791bUEqLS0lMTGRmJgYyzmj0UhMTAwJCQnVvichIaFKeYARI0ZcsXxpaSnz5s3D29ubiIgIyzV8fHwsyRFATEwMRqPxsq64SjNnzsTb29vyCA0NrdVnFaKhOZxZwMnsCzg71rPVs7OPw7In1ON+f2kUyRGAq5MD0W38gEbczQYQMQ6atoaiczIWSdRruiZIWVlZmEwmgoKqDv4MCgoiPT292vekp6fXqPyKFSvw8PDA1dWVDz74gLi4OPz9/S3XCAysOiPH0dERX1/fK9Y7bdo0cnNzLY+TJ0/W6rMK0dBUbk7bv60fbs71ZPXssmJYMgGKc6FFH4j5u94RWVWj3XbkzxwcYeBz6vGm2VBectXiQuhF9zFIWrnhhhtITk5m06ZNjBw5knvvvfeK45pqwsXFBS8vryoPIRqzerk57aoZkLYTmvjCPV+Do7PeEVnVkI7qH27bTmRTWFKuczQaihgHns0g/wzsWqx3NEJUS9cEyd/fHwcHBzIyMqqcz8jIIDi4+vEOwcHBNSrv7u5Ou3bt6NevH1988QWOjo588cUXlmtcmiyVl5eTnZ19xXqFsCfnCkosq2cPC68n6x8diYctFeMM7/wMvFvoG48GwvzcaOnrRplJIeHoOb3D0Y6jC0RPUY83fghmk67hCFEdXRMkZ2dnevXqRXx8vOWc2WwmPj6e6Ojoat8THR1dpTxAXFzcFcv/+bqVg6yjo6PJyckhMTHR8vrq1asxm81ERTWsFXiF0MKag2dRFOjczIuQ+rB6dlH2xcUg+zwKHYbrG49GDAaDfXSzAfSaCE2aQvZRdYsYIeoZ3bvYpk6dyueff878+fPZv38/Tz75JIWFhUyaNAmA8ePHM23axV2gn332WWJjY5k1axYHDhzg9ddfZ/v27UyZov41UlhYyF//+lc2b95MSkoKiYmJPPzww5w+fZp77rkHgE6dOjFy5EgeffRRtm7dyh9//MGUKVO47777qp3BJoS9qVeb0yoK/PyMut6Rfwe46Q29I9JUZYK09lBm453uD+rstT6PqMeb/61vLEJUQ/cEaezYsbz33ntMnz6dyMhIkpOTiY2NtQzETk1NJS0tzVK+f//+LFy4kHnz5hEREcEPP/zAsmXL6Nq1KwAODg4cOHCAMWPG0KFDB2677TbOnTvHhg0b6NKli+U6CxYsIDw8nGHDhjFq1CgGDhzIvHnzbPvhhaiHSspNrK9ovagX44+SF6otDEZHuOtzcHbTOyJNRbf1w8nBwMnsC5w418gXU+zzCBid4OQWdcsYIeoR3ddBaqhquo6CEA3N+kNnGf/lVgI8XdgybZi+C0RmH4e5A6G0AIZNv7gScyN3/+eb2XT0HK/f1pmJA1rrHY62fnwCdn4HXcfA3V/qHY2wAw1iHSQhRP1T2b02LDxQ3+TIbIafnlKTo5bRMOA5/WKxscH2Mg4J1LWsAPYug9xTuoYixJ9JgiSEsFAUhfgD9WR6f+JXkPIHOLnBnXMb9D5rtTW4vZogbT6WTUl5I5/h1aw7hA0CxQTbpQVJ1B+SIAkhLA5lFHDqvLp69oB2fvoFknsa4maox8NmQNMw/WLRQadmngR4unChzETiifN6h6O9vo+qz0nfyMKRot6QBEkIYVG5evYAPVfPVhRY8TyU5qurZVf+8rQjBoPBsr3LusN20M3WcZS6cGThWZnyL+oNSZCEEBaW8Ud6dq/t+R8c/g0cnOH22XbVtfZnld1s6w9l6RyJDTg4qesiAWz7j66hCFFJEiQhBABZBSXsOJkDwDC91j8qzIJfX1aPB78EgeH6xFEPDKxoQdqflkdmfrHO0dhAzwnqUg6pCZC+R+9ohJAESQihWnMgE0WBLiFeNPPWafXs2GnqLu+BXexq1lp1/D1c6NpcnYK88bAdtCJ5NYPwW9TjxK/0jUUIJEESQlTQfXPa4+th9/eAAe74pNFtRFsXF7vZ7GAcElzsZtu1BMou6BqKEJIgCSEoKTexoWIwsC7bi5jKYOVL6nGfydC8l+1jqIcGVSRIGw5nYTbbwZq+rYeCT0soyYV9y/WORtg5SZCEECQcPUdhqYlATxe6hnjbPoAtc+HsAXDzgxv/Zvv666lerZri7uzAucJS9qXl6R2O9oxG6DFePU76Rt9YhN2TBEkIwe/71NlrN3UOsv3q2XlpsPZt9Tjm7+oO7wIAZ0cj0W3V9ajW28N0f4DI+8FghJSNkHVE72iEHZMESQg7ZzYrxFUkSMO7BNs+gN//pm4n0rw3RD5g+/rrucptR+xmHJJ3c2h3k3qc/K2+sQi7JgmSEHYu+VQOZ/NL8HRxJLqNjVfPPr4B9vwAGOCW99QuFlFF5TikxJTzFJaU6xyNjfSoSJR3LVH35BNCB/LTSAg79/tetfVoaHggzo42/JHw54HZvR+GkB62q7sBCfNzI9S3CWUmhc3Hzukdjm20HwEu3pB3St2PTwgdSIIkhJ2L25cOwPDONp7enzQfzu6HJr4yMPsqDAaD/U33d3KFLneox7sW6xuLsFuSIAlhx45kFnD0bCFODgaGdgywXcXFebBmpno8dBq4+dqu7gboz9P97Ub3serzvp+gzA5WEhf1jiRIQtixysHZ0W398XR1sl3Ff3wIRVng2xZ6T7JdvQ1U/3Z+OBgNHMsq5GR2kd7h2EbL/uDVAkry4FCs3tEIOyQJkhB27Hc9utdyT0HCHPX4pjfUjUrFVXm5OtGzpQ9gR9P9jUbofo96vOt7fWMRdkkSJCHsVGZeMTtScwB1/SObWf0PKC+GVgMu7r0lrsnuxiEBdL9PfT78OxRl6xuLsDuSIAlhp1ZV7L0WGepDkJerbSo9kww7F6nHw98Eg40XpWzABlWsh7TpyDnKTXYy9T0wHIK7g7kM9v6odzTCzkiCJISdquxes1nrkaKoi0KiQLd7ZL+1WurW3BsfNyfyS8pJPpmjdzi2UzlYW7rZhI1JgiSEHcovLmPTEXVNnRFdbJQgHVkFJzaAgwvc+Jpt6mxEHIwGBrbzB+ysm63rGHXrkZObIfu43tEIOyIJkhB2aN2hs5SazLTxd6dtgIf2FSoKrH5TPe77KDRtpX2djZBlHJI9Tff3agath6jHu3/QNxZhVyRBEsIO/bb34ua0BluMAzqwAtJ2grMHDHxe+/oaqUEd1BakXadyyCkq1TkaG6rsZtu9RN84hF2RBEkIO1NcZmL1fjVBGtnVBpvTmk2w+i31uN+T4O6vfZ2NVDPvJnQI8sCswMYjdtSKFD4KHJwh6yBkHtA7GmEnJEESws5sOJxFYamJZt6uRLTw0b7CPUvVLUVcvSF6ivb1NXJ2Od3f1Rva3KAe71+ubyzCbkiCJISd+XVPGqC2HhmNGnevmcph7T/V4/7PQBMfbeuzA5XT/TcczkJRFJ2jsaHOFXuz7ftJ3ziE3ZAESQg7UlputmwvcnPXZtpXuPM7yD4Gbn4Q9YT29dmBqNa+uDgaScst5khmgd7h2E7Hm8HoCBl7IOuI3tEIOyAJkhB25I+jWeQXlxPg6UKvVk21ray8BNa9ox4PnAouNpgtZwdcnRzo21rd3HedPXWzuflenM22X1qRhPYkQRLCjsTuVheHHNklGAetu9eSvoHck+ARDH0ma1uXnRnyp242uyLdbMKG6kWCNGfOHMLCwnB1dSUqKoqtW7detfySJUsIDw/H1dWVbt26sXLlSstrZWVlvPLKK3Tr1g13d3dCQkIYP348Z86cqXKNsLAwDAZDlcfbb7+tyecToj4oN5ktq2ffrPXstfJS+OMj9XjQC+DURNv67MygioHaW46fo7jMpHM0NhR+Kxgc1CUjZNFIoTHdE6TFixczdepUZsyYQVJSEhEREYwYMYLMzMxqy2/atIlx48YxefJkduzYwejRoxk9ejR79uwBoKioiKSkJF577TWSkpJYunQpBw8e5Pbbb7/sWm+88QZpaWmWx9NPP63pZxVCT1uOZ3O+qAxfd2dLF41mdi2uaD0Kgp4PaVuXHeoQ5EGwlyvFZWa2nbCjTVzd/SBsoHoss9mExnRPkN5//30effRRJk2aROfOnZk7dy5ubm58+eWX1Zb/6KOPGDlyJC+99BKdOnXizTffpGfPnsyePRsAb29v4uLiuPfee+nYsSP9+vVj9uzZJCYmkpqaWuVanp6eBAcHWx7u7u6af14h9LJytzp7bXjnIBwdNPynbyqHje+rx/2fltYjDRgMBga1t8NtR0C62YTN6JoglZaWkpiYSExMjOWc0WgkJiaGhISEat+TkJBQpTzAiBEjrlgeIDc3F4PBgI+PT5Xzb7/9Nn5+fvTo0YN3332X8vLyK16jpKSEvLy8Kg8hGgqTWeG3vRXda900nr2290d15loTX+g1Sdu67Nggex2H1Ok2wACnEyEn9ZrFhagrXROkrKwsTCYTQUFVN8sMCgoiPT292vekp6fXqnxxcTGvvPIK48aNw8vLy3L+mWeeYdGiRaxZs4bHH3+cf/7zn7z88stXjHXmzJl4e3tbHqGhoTX9mELobvuJbLIKSvFydSS6jZ92FZnNsOE99Tj6LzJzTUOD2vljMMCB9Hwy8or1Dsd2PAKh1QD1eP/P+sYiGrU6JUjHjh2zdhyaKCsr495770VRFD799NMqr02dOpWhQ4fSvXt3nnjiCWbNmsUnn3xCSUlJtdeaNm0aubm5lsfJkydt8RGEsIpf96h/QNzUORhnRw3/LjqwAs4eABdv6PuYdvUImro70725N2CP3WwVY0qlm01oqE4/Kdu1a8cNN9zAt99+S3Fx3f9y8ff3x8HBgYyMjCrnMzIyCA6ufpZNcHBwjcpXJkcpKSnExcVVaT2qTlRUFOXl5Zw4caLa111cXPDy8qryEKIhMJsVy+rZo7ppOHtNUS62HkU9pm4PITQ12K672YCTWyC/+t4DIa5XnRKkpKQkunfvztSpUwkODubxxx+/5tT86jg7O9OrVy/i4+Mt58xmM/Hx8URHR1f7nujo6CrlAeLi4qqUr0yODh8+zKpVq/Dzu3aXQnJyMkajkcDAwFp/DiHqs6TU82TkleDh4sjA9hpuFHtklTr92skNop7Urh5hUTndf+ORLMxmO9p2xCsEQnqqx4d+0zcW0WjVKUGKjIzko48+4syZM3z55ZekpaUxcOBAunbtyvvvv8/ZszVv7p06dSqff/458+fPZ//+/Tz55JMUFhYyaZI6uHP8+PFMmzbNUv7ZZ58lNjaWWbNmceDAAV5//XW2b9/OlCnqJphlZWXcfffdbN++nQULFmAymUhPTyc9PZ3S0lJAHej94YcfsnPnTo4dO8aCBQt4/vnnefDBB2naVOPVhYWwsZ93qmuADe8chIujg3YVbZilPvd+WJ2OLTTXo6UPHi6OZBeWsudMrt7h2FbHUerzwV/1jUM0XooVFBcXK++//77i4uKiGAwGxcXFRXnooYeUM2fO1Oj9n3zyidKyZUvF2dlZ6du3r7J582bLa0OGDFEmTJhQpfz333+vdOjQQXF2dla6dOmi/PLLL5bXjh8/rgDVPtasWaMoiqIkJiYqUVFRire3t+Lq6qp06tRJ+ec//6kUFxfX+DPn5uYqgJKbm1vj9whha2XlJqXXm78rrV5Zoazen6FdRalbFWWGl6L83U9Rcmv2715Yx6PztymtXlmhfBJ/SO9QbCttt/qdezNQUUoK9Y5GNCA1/f1tUJS6bwe9fft2vvzySxYtWoS7uzsTJkxg8uTJnDp1ir///e/k5eXVqeutIcjLy8Pb25vc3FwZjyTqrY2Hs3jwiy34uDmx7f9icNJq/aPFD6kL90U+AKP/rU0dolr/3ZzCa8v20Le1L98/Xv3QhEZJUeDD7pCbCvd9B+Gj9I5INBA1/f1dp5+W77//Pt26daN///6cOXOGb775hpSUFP7xj3/QunVrBg0axNdff01SUlKdP4AQ4vpVdq/d3LWZdsnRuaMXp1v3l9XobW1IxTikpJTz5BeX6RyNDRkMF5OigyuvXlaIOqjTT8xPP/2U+++/n5SUFJYtW8att96K0Vj1UoGBgXzxxRdWCVIIUXsl5SbL7LXbI0K0qyhhDqBA++EQ2Em7ekS1Wvq50cbfnXKzwh9H7Gw2W8eb1edDseoaXEJYUZ0SpLi4OF555RWaNau6Iq+iKJbtPJydnZkwYcL1RyiEqJMNh7LIKy4n0NNFu73XCrMgeYF63P8ZbeoQ1zS0ozr7ds0BO1sPqdUAdc2twrPqytpCWFGdEqS2bduSlXX5XyrZ2dm0bt36uoMSQly/5RXda7d2D8HBaNCmkm3/gfJiaBZ5cRNRYXM3hKvdbGsOZnIdw0obHgcnaF+x9ZR0swkrq1OCdKV/gAUFBbi6ul5XQEKI61dUWk7cPnVB1dsiNNp7rbQIts5Tjwc8o44JEbro29qXJk4OZOaXsC/NzvaJlOn+QiOOtSk8depUQN1Jevr06bi5uVleM5lMbNmyhcjISKsGKISovdUHMrlQZiLUtwmRoT7aVLJzIRSdA5+W0OkObeoQNeLi6MCAdv6s2p/B2oNn6RJiR6uYtxsGRkc4u1/dJNm3jd4RiUaiVgnSjh07ALUFaffu3Tg7O1tec3Z2JiIighdffNG6EQoham15stq9dlv3EAxatOyYzRWDs4HoKeBQqx8lQgM3hAewan8Gaw5k8tQN7fQOx3aaNIVW/eH4ejgYq26SLIQV1Oqn2po1awCYNGkSH330kaz/I0Q9lFdcxtqD6mDd2yM1mr12+Hf1r3VXb3XtI6G7yoHaSannySkqxcfN+RrvaEQ6jqpIkFZKgiSspk5jkL766itJjoSop37bk06pyUz7QA86BnlqU8mWT9XnnuPBxUObOkStNPdpQscgT8wKrLe3zWs7jFSfUzbBhfP6xiIajRq3IN111118/fXXeHl5cdddd1217NKlS687MCFE3VTOXrstQqPutcz9cGwtGIzQ9zHrX1/U2dDwAA5m5LP2QKa2a1/VN76tIaCTOg7p6GroOkbviEQjUOMWJG9vb8sPW29v76s+hBD6SM8ttiwWODqyuTaVbJmrPoffog7QFvXGDRXdbGsPncVstqPp/gAdhqvPh+P0jUM0GjVuQfrqq6+qPRZC1B/Lkk9jVqBPWFNa+rld+w21VZQNOxerx1FPWv/64rr0atUUTxdHsgtL2XU6V7sZjPVRu5vgj4/UBMlsBqNGW+sIu1Gnb9CFCxcoKiqy/HdKSgoffvghv//+u9UCE0LUjqIo/C/xFABjerbQppKkb6D8AgR1U2cOiXrFycHIwPb+AKw5kKlzNDbWsh84e0JRFqTt0Dsa0QjUKUG64447+OabbwDIycmhb9++zJo1izvuuINPP/3UqgEKIWpm75k8DmcW4OxoZFR3DRaHNJXD1s/V435PyMKQ9ZSlm+2gnSVIDk7Q9gb1WLrZhBXUKUFKSkpi0KBBAPzwww8EBweTkpLCN998w8cff2zVAIUQNfO/JLX1aHjnILxcnaxfwYEVkHcK3Pyh693Wv76wiiEd1W1Hdp7KJaugROdobKx95Tgk6c0Q169OCVJRURGenur04d9//5277roLo9FIv379SElJsWqAQohrKzOZLYtData9Vjk4u/ckcJItheqrIC9XuoSoy7CsO2hnm9e2q9iX7XQSFNjZZxdWV6cEqV27dixbtoyTJ0/y22+/MXy4mrVnZmbK+khC6GD9obOcKyzF38OZQRVjUKwqbRekJqhbOvSebP3rC6uq7GZbbW/jkLyaQXA3QIGj8XpHIxq4OiVI06dP58UXXyQsLIyoqCiio6MBtTWpR48eVg1QCHFtS5NOA3BHZHMcHTSYvbPtP+pzp9vVX0KiXovpHASo45BKyk06R2Nj0s0mrKROP0nvvvtuUlNT2b59O7GxsZbzw4YN44MPPrBacEKIa8stKiNuXwYAd/XUYO2j4lzYvUQ97vOI9a8vrK57c28CPV0oLDWx+Vi23uHYVmWCdCQezHaWHAqrqvOfmsHBwfTo0QPjn9aa6Nu3L+Hh4VYJTAhRMyt2n6HUZCY82JPOzTTo4t65CMqK1JWKZWp/g2A0GhjWSW1FituXrnM0Nta8N7j6QHEOnNqudzSiAatTglRYWMhrr71G//79adeuHW3atKnyEELYTmX32l09m1t/axFFudi91meyTO1vQIZXdLOt2peJotjRqtoOjtBumHos3WziOtR4Je0/e+SRR1i3bh0PPfQQzZo102a/JyHENR3PKiQx5TxGg0Zbi5zYAFmHwNkDuo+1/vWFZqLb+uHm7EB6XjF7TufRrYUdbQPV7ibY8z81QRr2mt7RiAaqTgnSr7/+yi+//MKAAQOsHY8Qoha+334SgEHtAwj00mDqfWXrUfd7wVVmqDYkrk4ODG4fQOzedOL2Z9hZglQx3T99F+SlycQCUSd16mJr2rQpvr6+1o5FCFELZSYzP1RsLTKub6j1K8hLgwO/qMcytb9BirF0s2XoHImNeQRASE/1+MgqfWMRDVadEqQ333yT6dOnV9mPTQhhW2sOZHI2vwR/D2duDA+yfgVJ34C5HFpGQ3BX619faO7G8ECMBtiXlsep83b287qyFUnWQxJ1VKcutlmzZnH06FGCgoIICwvDyanqtgZJSUlWCU4IcWWLt6nda2N6tsDZ0cprH5nKIPEr9Vim9jdYvu7O9G7ly9YT2cTvz2RC/zC9Q7KddsNg/b/g6Bp1ur/RQe+IRANTpwRp9OjRVg5DCFEb6bnFrKnYjPTePhp0rx38FfLTwD0AOt1m/esLm4npHMjWE9nE7cuwrwSpeW9w8Van+5/ZAS166x2RaGDqlCDNmDHD2nEIIWrhh8STmBXoG+ZL2wAP61dQOTi7x0Pg6GL96wubualzMP9ceYDNx86RV1ymzUbG9ZGDI7QZDPt/hqOrJUEStVbndvmcnBz+85//MG3aNLKz1ZVak5KSOH36tNWCE0JczmxWWFwxe22sFq1HWYfh+DrAoG5MKxq01v7utA1wp9yssNbeNq9te6P6fETGIYnaq1OCtGvXLjp06MA777zDe++9R05ODgBLly5l2rRp1oxPCHGJhGPnOJl9AU8XR0Z102D68vYv1ecOI8GnpfWvL2zups7BgB3OZmtbsWDkqW3qljlC1EKdEqSpU6cyceJEDh8+jKvrxbVXRo0axfr1660WnBDicosqBmff0SOEJs5WHnhaWgg7FqjHMji70bipcyAAaw5mUlpu1jkaG2raCvzagWKCY+v0jkY0MHVKkLZt28bjjz9+2fnmzZuTnl77fX/mzJlDWFgYrq6uREVFsXXr1quWX7JkCeHh4bi6utKtWzdWrlxpea2srIxXXnmFbt264e7uTkhICOPHj+fMmTNVrpGdnc0DDzyAl5cXPj4+TJ48mYKCglrHLoQtnS8s5bc96r+x+/po0LqzZymU5ELTsIvdE6LBiwxtir+HC/nF5fxxNEvvcGyrshVJpvuLWqpTguTi4kJeXt5l5w8dOkRAQECtrrV48WKmTp3KjBkzSEpKIiIighEjRpCZmVlt+U2bNjFu3DgmT57Mjh07GD16NKNHj2bPnj0AFBUVkZSUxGuvvUZSUhJLly7l4MGD3H777VWu88ADD7B3717i4uJYsWIF69ev57HHHqtV7ELY2o87TlNqMtMlxIuuzTVYGTnxa/W510QwWnnpAKEbB6OBm7uq3Wwrd6XpHI2NVe7LdmS1uregEDVkUOqwi+EjjzzCuXPn+P777/H19WXXrl04ODgwevRoBg8ezIcffljja0VFRdGnTx9mz54NgNlsJjQ0lKeffppXX331svJjx46lsLCQFStWWM7169ePyMhI5s6dW20d27Zto2/fvqSkpNCyZUv2799P586d2bZtG717qzMbYmNjGTVqFKdOnSIkJOSacefl5eHt7U1ubi5eXrIFg9CeoigM/2A9hzMLeOOOLoyPDrNuBel7YO4AMDrC1P3gEWjd6wtdbT52jvvmbcbL1ZHtf7vJ+mtn1VelhfBOGJhKYcp28G+vd0RCZzX9/V2nfyGzZs2ioKCAgIAALly4wJAhQ2jXrh2enp689dZbNb5OaWkpiYmJxMTEXAzIaCQmJoaEhIRq35OQkFClPMCIESOuWB4gNzcXg8GAj4+P5Ro+Pj6W5AggJiYGo9HIli1bqr1GSUkJeXl5VR5C2NKW49kczizAzdmB0T002Jg2ab76HH6LJEeNUJ8wX/w9XMizt242Z3do2U89Prpa31hEg1KnBMnb25u4uDh++eUXPv74Y6ZMmcLKlStZt24d7u7uNb5OVlYWJpOJoKCq2yQEBQVdcSxTenp6rcoXFxfzyiuvMG7cOEummJ6eTmBg1V8Ajo6O+Pr6XvE6M2fOxNvb2/IIDdVgerUQV/HfzSkAjO7R3Ppr2ZQWwc7F6nGvida9tqgX7LqbrXIckkz3F7VQ6wTJbDbz5Zdfcuutt/L444/z6aefsnHjRs6cOUMdeus0VVZWxr333ouiKHz66afXda1p06aRm5treZw8edJKUQpxbZl5xZbB2Q9GtbJ+Bft+Ugdn+7SC1kOtf31RL9zSXV0W4re96fY1m61yHNKJDVBeom8sosGoVYKkKAq33347jzzyCKdPn6Zbt2506dKFlJQUJk6cyJ133lmryv39/XFwcCAjo+raHBkZGQQHB1f7nuDg4BqVr0yOUlJSiIuLq9LPGBwcfNkg8PLycrKzs69Yr4uLC15eXlUeQtjKom0nKTcr9G7VlM4hGnz3Kgdn9xwvg7MbMbvtZgvsAu6BUFYEqZv1jkY0ELX6Sfj111+zfv164uPj2bFjB9999x2LFi1i586drFq1itWrV/PNN9/U+HrOzs706tWL+PiLzZ5ms5n4+Hiio6OrfU90dHSV8gBxcXFVylcmR4cPH2bVqlX4+flddo2cnBwSExMt51avXo3ZbCYqKqrG8QthC+UmMwu3pALwULQGrUeZ++HkZjA4QI8HrX99UW/YbTeb0Xhx2QqZ7i9qqFYJ0nfffcdf//pXbrjhhsteu/HGG3n11VdZsGBBrQKYOnUqn3/+OfPnz2f//v08+eSTFBYWMmmSusXB+PHjq6zO/eyzzxIbG8usWbM4cOAAr7/+Otu3b2fKlCmAmhzdfffdbN++nQULFmAymUhPTyc9PZ3S0lIAOnXqxMiRI3n00UfZunUrf/zxB1OmTOG+++6r0Qw2IWxp1f5M0vOK8XN3ZmTX6ls4r0tSxR81HW8GTw2uL+oVu+9mOyIDtUXN1CpB2rVrFyNHjrzi6zfffDM7d+6sVQBjx47lvffeY/r06URGRpKcnExsbKxlIHZqaippaRf/0unfvz8LFy5k3rx5RERE8MMPP7Bs2TK6du0KwOnTp1m+fDmnTp0iMjKSZs2aWR6bNm2yXGfBggWEh4czbNgwRo0axcCBA5k3b16tYhfCFr6tGJw9tk8oLo5WXjm7rBiSF6rHMjjbLthtN1ubij/sM3ZDvp1tuSLqpFbrIDk7O5OSkkKzZtXv/3TmzBlat25NSUnjHwQn6yAJWzh6toBhs9ZhMMCGl2+gRVM361aw63tY+ih4h8KzO8Fo5QRM1EuvLdvDfzencE+vFrx7T4Te4djOZ4MhbSfc+RlE3Kd3NEInmqyDZDKZcHR0vOLrDg4OlJeX1+aSQoirWLBZHXs0LDzQ+skRQGLF2kc9HpLkyI7YbTebTPcXtXDlbKcaiqIwceJEXFxcqn3dHlqOhLCVotJyliSqy0k82E+DwdlZhyFlIxiMMjjbzlR2s2UVlPDH0Sxu6GgnC4O2GwYb31cXjDSbZcamuKpaJUgTJky4Zpnx48fXORghxEXLdpwhv7iclr5uDG5fuz0Oa6Ry5ez2w8Fbg5W5Rb1VOZvtv5tT+HnnGftJkFr0BWcPKMqC9F0QEql3RKIeq1WC9NVXX2kVhxDiTxRF4etNxwEYH90Ko9Fg3QrKS2Rwtp27PTKE/25O4bc96VwYbaKJsx10sTo6Q9ggOPSrOt1fEiRxFdK+KEQ99MeRcxzKKMDd2YF7+2iwrc2BFVB0DjybQbubrH99Ue/1btWUFk2bUFhq4vd91W+x1CjJdH9RQ5IgCVEPffWH2np0d68W1t93DaoOznaoVUOyaCQMBgN3Vmx6vGzHaZ2jsaHKBSNPboGSAn1jEfWaJEhC1DPHswqJP6BuhTNxQGvrV5B9DI6vAwzQ8yHrX180GKMrEqT1h7M4m28nk2z82kLTMDCXwYmNekcj6jFJkISoZ+ZvOgHAjeGBtPZ3t34FlStntxsGPi2tf33RYLQN8CCihTcms8KKXWf0Dsd2ZNsRUQOSIAlRj+QVl7Fkuzq1/2EtWo/KS2HHt+qxDM4WYOlmW5pkT91sFeOQjso4JHFlkiAJUY98v+0khaUmOgR5MKCd37XfUFuHfoXCs+rO5h2uvG2QsB+3RYTgaDSw+3Qu+9Py9A7HNloPUjdnPncEzqfoHY2opyRBEqKeMJkV5iecAGBi/9YYDFae2g9/Gpz9IDhoMPhbNDh+Hi7EdFL3vvy+ovWy0XP1htC+6rG0IokrkARJiHoifn8GJ7Mv4OPmZOn2sKrzKRd/GcjgbPEnYyuWkli24zQl5Sado7ERGYckrkESJCHqiS8rpvaP69tSm0X7dvwXUKD1EPBtY/3riwZrUHt/grxcOF9Uxqp9mXqHYxuV45COrQeT7CEqLicJkhD1wO5TuWw+lo2j0cD4aA32XTOVy+BscUWODkbu7tUCsKNutpBIaNIUSnLhdKLe0Yh6SBIkIeqBzzccA9QBs828m1i/giNxkJ8Gbn4Qfov1ry8avHt6qd1s6w+f5XTOBZ2jsQGjA7QZqh7LOCRRDUmQhNDZqfNF/LI7DYBHBmkwtR8uDs6OGAeOLtrUIRq0MH93+rf1Q1Hguy2peodjGzIOSVyFJEhC6OyrP05gMisMbOdPlxBv61eQdwYO/6Ye95xg/euLRuOhfmr37qJtqZSWm3WOxgYqE6TTiXDhvL6xiHpHEiQhdJR7oYxFW9W/1h8drNHA6R0LQDFDy/4Q0EGbOkSjENM5iEBPF7IKSondawcb2Hq3AP+O6r+PY+v0jkbUM5IgCaGj77amUlhqomOQJ4Pb+1u/ArP54tYivaT1SFydk4ORcX3V7We+TbCTBRTbyaraonqSIAmhk9JyM1//cQJQxx5psjDksdWQm6oujNf5DutfXzQ64/q2xMFoYOuJbA6m5+sdjvYs45BWg6LoG4uoVyRBEkInK3adIT2vmEBPF26PDNGmksrB2d3HgpMGs+NEoxPs7crwzurK2l9VrM3VqLUaAA7OkHsSsg7rHY2oRyRBEkIHiqIwb706tX/igDBcHDVYGLIgEw6uVI9lcLaohYcHqrMpl+44TVZBic7RaMzZDVpGq8fSzSb+RBIkIXSw4XAWB9LzcXN24IG+GiwMCZC8EMzl0LwXBHfVpg7RKPVu1ZSIUB9Ky8381x7GIlnGIcl0f3GRJEhC6GDuuqOAugeWt5sGm8Yqyp8GZ0+0/vVFo2YwGHikohXp280pFJc18v3ZKrcdObERyht5i5moMUmQhLCxnSdz2HT0HI5GA48M0mhq/4mNkH0UnD2gy13a1CEatZu7BtPcpwnnCkv5ccdpvcPRVlAX8AiCsiJI3ax3NKKekARJCBurbD26I7I5zX00GjidVDE4u9vd4OKhTR2iUXN0MDJpQBgAn607isnciGd4GQxVZ7MJgSRIQtjU0bMFlgX4nhiiUetRUTbsW64ey+BscR3G9W1JUzcnTpwrYsWuM3qHoy3ZdkRcQhIkIWxo3rpjKArEdAqifZCnNpXsWgymEgjuBiE9tKlD2AV3F0cmV4xFmr36CObG3IrU5gb1OX23OgNU2D1JkISwkfTcYpbuOAXAk0PbalOJokDi1+pxzwlq14EQ12F8/zA8XR05nFnQuLcf8QiA4O7q8dE1+sYi6gVJkISwkS//OE6ZSaFva196tWqqTSUnt8LZA+DYBLrfq00dwq54uToxqX8YAB/HH27crUiy7Yj4E0mQhLCB3KIyFmxW15N5cohGrUdwcXB2lzvV7UWEsIKHB7bG08WRA+n5/NyYxyL9eaC22axvLEJ3uidIc+bMISwsDFdXV6Kioti6detVyy9ZsoTw8HBcXV3p1q0bK1eurPL60qVLGT58OH5+fhgMBpKTky+7xtChQzEYDFUeTzzxhDU/lhBV/HfzCQpLTYQHezK0Y4A2lRTnwp6l6rFsTCusyMfNmccrJhXM+v0QpeWNNHkIjQIndyjMhMy9ekcjdKZrgrR48WKmTp3KjBkzSEpKIiIighEjRpCZWf0AuU2bNjFu3DgmT57Mjh07GD16NKNHj2bPnj2WMoWFhQwcOJB33nnnqnU/+uijpKWlWR7/+te/rPrZhKhUXGbiq4pNaZ8c2labTWkBdi+B8gsQEK7+oBfCih4e2JoATxdSs4tYtC1V73C04egCYQPV4yMym83e6Zogvf/++zz66KNMmjSJzp07M3fuXNzc3Pjyyy+rLf/RRx8xcuRIXnrpJTp16sSbb75Jz549mT17tqXMQw89xPTp04mJiblq3W5ubgQHB1seXl5eVv1sQlRasv0k5wpLadG0Cbd0a6ZdRZUb08rgbKEBN2dHnhnWHlDHIhWUlOsckUZkHJKooFuCVFpaSmJiYpVExmg0EhMTQ0JCQrXvSUhIuCzxGTFixBXLX82CBQvw9/ena9euTJs2jaKioquWLykpIS8vr8pDiGspN5n5rGJT2scGt8HRQaN/cmd2QPoudVfyiPu0qUPYvfv6hNLa352sglI+WX1Y73C0UTkOKTUBSgv1jUXoSrcEKSsrC5PJRFBQUJXzQUFBpKdXP5U0PT29VuWv5P777+fbb79lzZo1TJs2jf/+9788+OCDV33PzJkz8fb2tjxCQ0NrVaewT7/sTuPU+Qv4uTtzTy8NvzOVrUedbgc3X+3qEXbNycHIa7d2AuDLjcc5klmgc0Qa8GsH3i3BVAopm/SORuhI90HaenjssccYMWIE3bp144EHHuCbb77hxx9/5OjRo1d8z7Rp08jNzbU8Tp48acOIRUOkKAqfrlW/U5MGhNHE2UGbikoKYPcP6rEMzhYauzE8iGHhgZSZFP7+814UpZFN+zcYoG3FopEyDsmu6ZYg+fv74+DgQEZGRpXzGRkZBAcHV/ue4ODgWpWvqagodUDrkSNHrljGxcUFLy+vKg8hrmbNwUwOpOfj7uzAQ/3CtKto71IozQffNhA2SLt6hKgw/bbOODsY2XA4i5W7G+HikTIOSaBjguTs7EyvXr2Ij7+YoZvNZuLj44mOjq72PdHR0VXKA8TFxV2xfE1VLgXQrJmGA2iFXVEUhdmr1YT7wX6t8HZz0q6ybV+oz70myuBsYROt/NwtewnOWL6H7MJSnSOystaDwWCErIOQe0rvaIROdO1imzp1Kp9//jnz589n//79PPnkkxQWFjJp0iQAxo8fz7Rp0yzln332WWJjY5k1axYHDhzg9ddfZ/v27UyZMsVSJjs7m+TkZPbt2wfAwYMHSU5OtoxTOnr0KG+++SaJiYmcOHGC5cuXM378eAYPHkz37t1t+OlFY5Zw7BxJqTk4Oxote1lp4nQSpCWrg7Mjrz6OTghreurGdrQP9CCroJS//9zI1gxq0hSa91aPpRXJbumaII0dO5b33nuP6dOnExkZSXJyMrGxsZaB2KmpqaSlpVnK9+/fn4ULFzJv3jwiIiL44YcfWLZsGV27drWUWb58OT169OCWW24B4L777qNHjx7MnTsXUFuuVq1axfDhwwkPD+eFF15gzJgx/Pzzzzb85KKxm7NGbT0a2zuUQC9X7SraXtF61Hk0uPtpV48Ql3BxdOC9eyIwGuCn5DP81tj2aauczSbjkOyWQWl0I+xsIy8vD29vb3Jzc2U8kqhiR+p57vz3JhyNBta+NJQWTd20qehCDswKVxeHnBQLra6vq1mIungn9gCfrj1KUzcnVj47iGbeTfQOyTpOboUvbgJXH3j5GBg1mmQhbK6mv7/tchabEFqqbD0a3aO5dskRwK7FanIU2Bla9tOuHiGu4rmY9nRt7sX5ojKe+W4H5aZGsg1JSE91P8PiHLUrW9gdSZCEsKL9aXms2p+JwaBuK6IZRbk4OLv3wzI4W+jGxdGBOff3xNPFkW0nzjMr7pDeIVmHgyO0qZzuH6dvLEIXkiAJYUX/rlj3aFS3ZrQN8NCuopRN6gwbJ3foPla7eoSogVZ+7rw9Rp3k8unaoyzfeUbniKyk/XD1+fDv+sYhdCEJkhBWcjyrkF92qb8YnhraTtvKKgdnd7sbXGUMnNDfLd2b8dhgder/i0t2kpR6XueIrKBdxdZWZ3ZAQfWbqIvGSxIkIazk07VHMCswLDyQziEaJi0FZ2HfcvW4z2Tt6hGill4ZGU5MpyBKy8089s12TmQ18L3MPIOgWaR6fGSVrqEI25MESQgrOJ1zgaVJpwF1fRhNJX8L5jJo3guaRWhblxC14GA08NF9kXRu5kVWQSkP/GcLp85ffSPwek+62eyWJEhCWMG8dUcpNytEt/GjZ8um2lVkNsP2r9Tj3tJ6JOofdxdH5j/clzYB7pzOucD9n28hLfeC3mHVXWWCdGQ1mMr1jUXYlCRIQlyns/klLNqmbl48RevWo6OrISdFnX7c5U5t6xKijgI8XVj4SD9a+rqRml3E3Z8mcCSzQO+w6qZ5T2jiCyW5cGqr3tEIG5IESYjr9J+NxygpNxMZ6kP/thqvZl05ODvyAXDWcI0lIa5TsLcr3z3Wz9KSdM/cTSSmNMCB20aHi4O1pZvNrkiCJMR1yCkq5duEFACm3NAOg5brEeWegkOx6nGvSdrVI4SVNPdpwg9P9Cci1IfzRWWMm7eZxdtS9Q6r9izjkGQ9JHsiCZIQ1+HrTScoLDURHuzJsE6B2laWOB8UM4QNgoAO2tYlhJX4ujuz8JEohncOotRk5pX/7Wba0t1cKDXpHVrNtRsGGCBjD+Se1jsaYSOSIAlRRwUl5Xz1xwkAntK69ai8FBK/Vo97P6xdPUJowN3FkbkP9uLF4R0wGOC7ranc+skGdp/K1Tu0mnHzhRZ91GNZVdtuSIIkRB0t2JxC7oUy2vi7M6pbM20r27cMCjPBMwQ63aZtXUJowGg0MOXG9nzzcF+CvFw4eraQO//9B2/9so+CkgYwO0y62eyOJEhC1EFxmYnPNxwH4ImhbXEwarwX2pbP1OfeD4ODk7Z1CaGhQe0DiH12MLd0a0a5WeHzDce58b21/JR8GkVR9A7vytrfpD4fWwvlJbqGImxDEiQh6mDxtpNkFZTQ3KcJd/Zorm1lpxLh9HZwcIZeE7WtSwgbaOruzJwHevLVxD6E+bmRmV/Cs4uSGTtvM/vO5OkdXvWCu4NHEJQWqHshikZPEiQhaqmk3MTcdeqmtE8MbYuTg8b/jLZWtB51HQMeAdrWJYQN3RAeyG/PD+alER1xdTKy9Xg2t3yygecXJ3Myu56twG00QruKVqRDv+kbi7AJSZCEqKUfEk+RlltMkJcL9/RqoW1lBZmwZ6l63PcxbesSQgcujg48dUM74l8Yyq3dm6Eo8OOO09w4ay2vL9/L2fx61J3V8Wb1+eBKqM/dgcIqJEESohbKTGb+vaai9WhIW1ydHLStMPFrdd+1Fn3UFX2FaKSa+zRh9v09+XnKQAa196fMpPD1phMMeXcN7/9+kPziMr1DhLY3gKOrupp95n69oxEakwRJiFr4Mek0p3Mu4O/hwri+LbWtzFQG2ypWzu77uLZ1CVFPdGvhzX8nR7HwkSgiQn0oKjXx8eojDP7XGv6z4RjFZTqun+TsDm2GqscHV+oXh7AJSZCEqKFyk5k5a48A8PjgNtq3Hu37CQrS1YGhne/Qti4h6pn+7fxZ9pf+zH2wJ20C3DlfVMY/ftnPje+t5fvtJyk3mfUJzNLN9qs+9QubkQRJiBpavvMMKeeK8HV35oF+GrceAWydpz73fhgcnbWvT4h6xmAwMLJrM35/bjDvjOlGM29XzuQW8/IPuxj50QZ+35tu+6UBOoxUn09vh/wM29YtbEoSJCFqwGRWmL1GbT16ZFBr3Jwdta3wTDKc3AJGJ9l3Tdg9RwcjY/u0ZM2LQ/m/UZ3wcXPiSGYBj/03kYlfbSPlXKHtgvEMhua91OPKvRFFoyQJkhA18MvuNI6dLcTHzYnx0WHaV1jZetRlNHgGaV+fEA2Aq5MDjw5uw/qXb+AvQ9vi7GBk3aGzDP9gPfPWH8VktlFrknSz2QVJkIS4BrNZYfbqwwA8PKA1Hi4atx4VZsHuH9RjGZwtxGW8XJ14eWQ4sc8NYkA7P0rKzfxz5QHu/SyBE1k2aE3qOEp9PrYGSuvZek3CaiRBEuIaftubzqGMAjxdHZnQP0z7CpPmg6kEQnpAi97a1ydEA9UmwINvJ0fxzphueLg4kphynls+3sBPyae1rTiwM/i0hPJidesR0ShJgiTEVSiKwser1bFHk/qH4d1E433Q/jy1P+oJMGi8x5sQDZzBYGBsn5b89vxg+rb2pbDUxLOLknn1f7soKddoSQCD4WIrkkz3b7QkQRLiKlbtz2R/Wh7uzg48PLC19hXu+wnyToN7AHS5U/v6hGgkmvs0YeEjUTwzrD0GAyzadpJx8zaTmV+sTYWV45AOxYJZx7WZhGYkQRLiChRF4eN4dezR+P5h+LhpPNVeUWDTJ+px38fA0UXb+oRoZBwdjEy9qQNfT+qLl6sjSak53P7JHxxI12AD3FYDwMUbCs/C6UTrX1/oThIkIa5g7aGz7D6dSxMnBx6xRetRyh+QlgyOTaD3ZO3rE6KRGtIhgJ+mDKRtgDvpecXc82kCCUfPWbcSBydoH6MeH/jFutcW9YIkSEJU48+tRw9Ft8LPwwatOQlz1OfIceDup319QjRirf3dWfrkAPqG+ZJfUs6EL7cSuyfdupWE36I+7/9ZNq9thCRBEqIafxw5x47UHFwcjTwyyAatR1lHLq6p0u8v2tcnhB3wdnPim8l9GdklmFKTmacWJvHLrjTrVdB+ODi4QPZRyNxnveuKekH3BGnOnDmEhYXh6upKVFQUW7duvWr5JUuWEB4ejqurK926dWPlyqozCJYuXcrw4cPx8/PDYDCQnJx82TWKi4t56qmn8PPzw8PDgzFjxpCRIUvGi4sqW4/uj2pJoKer9hVu/jegQIebwb+99vUJYSdcnRyYfX8P7uzRHJNZ4envkli+84x1Lu7iCe0qutn2/WSda4p6Q9cEafHixUydOpUZM2aQlJREREQEI0aMIDMzs9rymzZtYty4cUyePJkdO3YwevRoRo8ezZ49eyxlCgsLGThwIO+8884V633++ef5+eefWbJkCevWrePMmTPcddddVv98omFKOHqOrSeycXYw8vjgttpXWJQNyQvV4+intK9PCDvj6GDkvXsiuLtXC8wKPL84mbh9VvqjuHIjaUmQGh2DYvOd/i6KioqiT58+zJ49GwCz2UxoaChPP/00r7766mXlx44dS2FhIStWrLCc69evH5GRkcydO7dK2RMnTtC6dWt27NhBZGSk5Xxubi4BAQEsXLiQu+++G4ADBw7QqVMnEhIS6NevX7WxlpSUUFJSYvnvvLw8QkNDyc3NxcvLq873QNQviqIw9rPNbD2RzUP9WvHm6K7aV7ruXVjzD2gWAY+tk7WPhNCI2azw4pKdLN1xGmdHI19N7MOAdv7Xd9HiXPhXWzCXwV+2QGC4dYIVmsnLy8Pb2/uav791a0EqLS0lMTGRmJiYi8EYjcTExJCQkFDtexISEqqUBxgxYsQVy1cnMTGRsrKyKtcJDw+nZcuWV73OzJkz8fb2tjxCQ0NrXKdoODZVth45GnnqhnbaV1hecnHftegpkhwJoSGj0cC/7u7OiC5BlJabeeyb7ew5nXt9F3X1hrY3qMf7l19/kKLe0C1BysrKwmQyERRUdSPOoKAg0tOrn2mQnp5eq/JXuoazszM+Pj61us60adPIzc21PE6ePFnjOkXDoCgK78cdAuD+vi0J9rbB2KOdi6AwE7yay8KQQtiAo4ORj8f1YEA7PwpLTTz89TZO51y4votautkkQWpMdB+k3VC4uLjg5eVV5SEalw2Hs0hMOY+Lo5G/DLXB2COzCf74SD2OfkpdV0UIoTkXRwc+fbAXHYM8ycwvYdJXW8krLqv7BTuOAqMjZOyGc0etF6jQlW4Jkr+/Pw4ODpfNHsvIyCA4OLja9wQHB9eq/JWuUVpaSk5OznVdRzQuf249erBfKwK9bNB6tP9ndXqwqw/0nKB9fUIICy9XJ76a1IcgLxcOZRQwdXEyZnMdh+S6+ULrweqxDNZuNHRLkJydnenVqxfx8fGWc2azmfj4eKKjo6t9T3R0dJXyAHFxcVcsX51evXrh5ORU5ToHDx4kNTW1VtcRjcvag2dJPpmDq5ORJ4bYoPVIUWDjB+px1OPg4qF9nUKIKkJ8mvCf8X1wdjSyan8mH1Us71EnnW5XnyVBajR07WKbOnUqn3/+OfPnz2f//v08+eSTFBYWMmnSJADGjx/PtGnTLOWfffZZYmNjmTVrFgcOHOD1119n+/btTJkyxVImOzub5ORk9u1TF+06ePAgycnJlvFF3t7eTJ48malTp7JmzRoSExOZNGkS0dHRV5zBJho3RVH4YJXaejQ+OowATxusmn1szcVtRfo+rn19QohqdWvhzcw7uwHwUfzhuk//D78VDEb13/X5E1aLT+hH1wRp7NixvPfee0yfPp3IyEiSk5OJjY21DMROTU0lLe3iqqf9+/dn4cKFzJs3j4iICH744QeWLVtG164Xp2IvX76cHj16cMst6hLw9913Hz169KiyDMAHH3zArbfeypgxYxg8eDDBwcEsXbrURp9a1Dfx+zPZdSoXN2cHHh/cxjaVVrYe9Zog24oIobMxvVowsX8YoK6RdCSzoPYX8QhQN7AFtftcNHi6roPUkNV0HQVRvymKwq2fbGTvmTyeGNKWV2+2wRompxLhPzeqgzqf2QE+LbWvUwhxVWUmMw98voWtJ7JpG+DOsqcG4Olay4kTWz+HlS9C817w6GptAhXXrd6vgyREffD7vgz2nsnD3dmBx2zVevRHRetRt3skORKinnByMDLngZ4Ee7ly9GwhLy7ZSa3bDzrdrnaznU6U2WyNgCRIwm6ZzQofVMxcmzSgNb7uztpXevYQ7K9YCX7As9rXJ4SosQBPF+Y+1AsnBwO/7c3g2y2ptbuAZxC0Gaoe7/7B6vEJ25IESdit2L3pHEjPx9PFkUcGtbZNpRtmAYq6bkpgJ9vUKYSoschQH14ZqXa1/2PFPg6m59fuAt3Hqs+7FquzVUWDJQmSsEtms8JHq9QpvZMGtsbHzQatR+eOwu7v1ePBL2lfnxCiTh4e0JqhHQMoKTfz9HdJFJeZav7m8FvU2anZR+FMknZBCs1JgiTs0vKdZziYkY+nqyOTB9qw9UgxQ/vh0LynbeoUQtSa0WjgvXsi8PdQF5F865f9NX+zi6eaJAHs+l6bAIVNSIIk7E5pudmyavYTQ9ri3cQGW3xkH1f3XQMY/LL29Qkhrou/hwvv3xsBwH83p/Db3prv+Un3e9XnPf8DU7kG0QlbkARJ2J3F20+Sml2Ev4cLkwaE2abSDbNAMUHbGyG0j23qFEJcl8EdAixro73yv12k5dZwU9u2N4KbHxSehWNrtQtQaEoSJGFXLpSa+KRiO4Gnb2yHm7Oj9pWeT4Gd36nHQ17Vvj4hhNW8MLwj3Vt4k1NUxss/7KrZ1H8HJ+hyl3q8W7rZGipJkIRdmZ9wgsz8Epr7NOG+vqG2qXTjB2Auh9ZDoGWUbeoUQliFs6ORD8ZG4uJoZMPhLBZureHU/8rZbPtXQGmhdgEKzUiCJOxG7oUyPl2rLt72/E0dcHF00L7SnJOw41v1eKi0HgnRELUN8LBM/X/rl/2knKtBwtOiNzRtDWWFcGClxhEKLUiCJOzG5+uPkXuhjPaBHtzZo7ltKt34PpjLIGwQtOpvmzqFEFY3sX8Y/dr4UlRq4qUluzCZr9HVZjBcHKy9a7H2AQqrkwRJ2IWz+SV8+cdxQB1T4GA0aF9p9jFI+kY9HjpN+/qEEJoxGg28e3cE7s4ObD2RzZcbj1/7Td0qEqSjqyG/FrPgRL0gCZKwC7NXH6ao1EREqA8jugTZptK1b6tjj9oOg7ABtqlTCKGZUF83Xru1MwDv/n6QwxnXWGXbvx2ERqkzWJMX2CBCYU2SIIlG79jZAhZU7Kn0yoiOGAw2aD3K2Htxkbhhr2lfnxDCJsb2CWVoxwBKy81M/X4n5Sbz1d/Qa6L6nPQNmK9RVtQrkiCJRu9fsQcpNyvc0DGA/u38bVPp6rcABTrfASE9bFOnEEJzBoOBd8Z0x8vVkd2nc/ls/bGrv6HzaHDxhvMn4Pg6W4QorEQSJNGobT+RTezedIwGmDbKRpvDntoOB38BgxFu+D/b1CmEsJkgL1dev70LAB+uOnT1DW2d3S4O1k6ab4PohLVIgiQaLUVReGuluofSvb1D6RDkaZuK499QnyPGQUBH29QphLCpO3s0J6ZTIGUmhZd+uEZXW68J6vP+FVCYZZsAxXWTBEk0Wit3p7MjNYcmTg5MvamDbSo9tlZtRjc6wZBXbFOnEMLmDAYDb93ZDS9XR3adukZXW3A3aN5LXfIjeaHtghTXRRIk0SiVlpv5128HAHhscBsCvVy1r9RshlV/V497PwxNW2lfpxBCN3/uavto1eGrd7X1rGhFSpoPNdmuROhOEiTRKH27OYWUc0UEeLrwWMVmk5rb8z84kwTOHjDoBdvUKYTQ1Z09mjMsPJBSk/nqXW1dx6g/G84dgZQ/bBukqBNJkESjk1tUxser1Q1pn4/pgLuLDTakLbsAq15Xjwc+D542WmtJCKErg8HAP++qQVebiwd0u1s9TvzaZvGJupMESTQ6H6w6RE6RuqXIvb1b2KbShDmQdwq8WkD0U7apUwhRL9S4q61yTaR9y6Eo2zbBiTqTBEk0KgfT8/nv5hQAZtzWBUcHG3zFCzJh4wfqccwMcGqifZ1CiHqlRl1tIT2gWQSYSi5uQyTqLUmQRKOhKAqvL9+LyawwskswA9vbaFHINW9BaQGE9ISud9umTiFEvVLjrra+j6vPWz8HU7ntAhS1JgmSaDR+3ZNOwrFzuDga+b9bbLQoZMa+i38JjvgnGOWflBD2KsjLlRm3XaOrresYcPNXu+QPrLBxhKI25Ke5aBQulJp46xd1UcjHh7Ql1NdN+0oVBX7/P1DM0Ol2aBWtfZ1CiHrtrp7X6GpzcoXek9TjLXNtH6CoMUmQRKMwd91RTudcIMTblSeHtLVNpQdWwNHV6qKQN/3dNnUKIeq1GnW19Z4MRkdITYAzO2wfpKgRSZBEg3cyu4i5644C8H+3dKaJs4P2lZYWwq+vqscDngFfG621JISo9y7tajuUcUlXm1cz6HKXerzpExtHJ2pKEiTR4L2xYh8l5Wb6tfFlVLdg21S6/l11DIF3Sxj0om3qFEI0GH/uantxSTVdbf2fVp/3/gjnT9g8PnFtkiCJBi12Tzpx+zJwNBp4/fYuGAwG7Ss9ewg2zVaPb35H3a1bCCH+5NKutnkbLulqa9Yd2g5TxzAmzNEnSHFV9SJBmjNnDmFhYbi6uhIVFcXWrVuvWn7JkiWEh4fj6upKt27dWLlyZZXXFUVh+vTpNGvWjCZNmhATE8Phw4erlAkLC8NgMFR5vP3221b/bEI7+cVlzFi+B4DHh7QhPNhL+0oVBVa+oG462WEkhI/Svk4hRIP05662D+Oq6Wob8Kz6nPRfKDxn4+jEteieIC1evJipU6cyY8YMkpKSiIiIYMSIEWRmZlZbftOmTYwbN47JkyezY8cORo8ezejRo9mzZ4+lzL/+9S8+/vhj5s6dy5YtW3B3d2fEiBEUFxdXudYbb7xBWlqa5fH0009r+lmFdb3720Ey8koI83Pj6Rvb26bSPf+D4+vB0VVtPRJCiKu4q2dzbrxSV1vrwdAsEsovwJZPdYtRVE/3BOn999/n0UcfZdKkSXTu3Jm5c+fi5ubGl19+WW35jz76iJEjR/LSSy/RqVMn3nzzTXr27Mns2WqXh6IofPjhh/ztb3/jjjvuoHv37nzzzTecOXOGZcuWVbmWp6cnwcHBloe7u7vWH1dYSWLKecuK2W/d2Q1XJxsMzC7Og9/+Tz0e9AI0DdO+TiFEg2YwGJh5pa42g+HixtZbPoML5/UJUlRL1wSptLSUxMREYmJiLOeMRiMxMTEkJCRU+56EhIQq5QFGjBhhKX/8+HHS09OrlPH29iYqKuqya7799tv4+fnRo0cP3n33XcrLr7yqaUlJCXl5eVUeQh+l5Wb+unQ3igJjerZgQDsbrZj9+9+gIB1820L/Z2xTpxCiwQvycmX6lbrawm+FwM5QkgebZV2k+kTXBCkrKwuTyURQUNWdz4OCgkhPT6/2Penp6VctX/l8rWs+88wzLFq0iDVr1vD444/zz3/+k5dffvmKsc6cORNvb2/LIzQ0tOYfVFjV5xuOcTAjH193Z9utmH10DSTNV49v/0Rd7E0IIWpozJ+62p5dlExJuUl9wWiEIRW/ezZ/CsW5+gUpqtC9i00vU6dOZejQoXTv3p0nnniCWbNm8cknn1BSUlJt+WnTppGbm2t5nDx50sYRC4AjmQV8FK8OuH/t1k74ujtrX2lJPiyvaDHq+xiEDdC+TiFEo2IwGHj7rm74ujuzPy2Pf8UevPhipzsgIBxKctUkSdQLuiZI/v7+ODg4kJGRUeV8RkYGwcHVr2cTHBx81fKVz7W5JkBUVBTl5eWcOHGi2tddXFzw8vKq8hC2VWYyM/X7ZErLzQxq78/oyOa2qThuBuSmgk8rGDbDNnUKIRqdQC9X3r27OwBfbDzO+kNn1ReMRhjyinq8abbMaKsndE2QnJ2d6dWrF/Hx8ZZzZrOZ+Ph4oqOr39cqOjq6SnmAuLg4S/nWrVsTHBxcpUxeXh5btmy54jUBkpOTMRqNBAYGXs9HEhqavfoIu07l4uXqyLt3R9hmzaPj62H7F+rx7Z+Ai4f2dQohGq1hnYIYH90KgBeW7ORcQUWvRefR0CwCSvNh4/v6BSgsdO9imzp1Kp9//jnz589n//79PPnkkxQWFjJpkrqZ3/jx45k2bZql/LPPPktsbCyzZs3iwIEDvP7662zfvp0pU6YAajPmc889xz/+8Q+WL1/O7t27GT9+PCEhIYwePRpQB3p/+OGH7Ny5k2PHjrFgwQKef/55HnzwQZo2bWrzeyCuLflkDrPXHAHgH3d2I9jbBmOASgvhJ/V7Re+Hoc0Q7esUQjR6fx3ViQ5BHpzNL+HlH3ahKIrailTZQr11HuTIMA696Z4gjR07lvfee4/p06cTGRlJcnIysbGxlkHWqamppKWlWcr379+fhQsXMm/ePCIiIvjhhx9YtmwZXbt2tZR5+eWXefrpp3nsscfo06cPBQUFxMbG4uqq/lJ1cXFh0aJFDBkyhC5duvDWW2/x/PPPM2/ePNt+eFEjF0pNTF2cjMmscFtECLdHhNim4thXIScFvEPhpjdsU6cQotFzdXLg43E9cHY0En8gk28rliyh7Y0QNghMpbDmLX2DFBgURVH0DqIhysvLw9vbm9zcXBmPpLHpP+3hm4QUgr1ciX1uED5uNhiYved/8MPDgAEmLFcXdBNCCCv66o/j/P3nfTg7Gln6ZH+6NveGU4nwnxvVAo+uhua99A2yEarp72/dW5CEuJp1h87yTYL619W793S3TXJ0/gT8/Jx6POgFSY6EEJqY2D+MmE6BlJabeXJBIrlFZdCiF3S/Ty0QO03d3kjoQhIkUW+l5xbzwvfJAEyIbsWg9gHaV2oqg/89oi7a1qIvDH1V+zqFEHbJYDAw655IWvq6cTL7As9/n4zZrEDMDHByg5Nb1NZsoQtJkES9VGYy89TCJLIKSgkP9uTVm220IOSaf8KpbeDiDWP+Aw5OtqlXCGGXvN2c+PTBnrg4Gll9IJN/rz0CXiEw8Hm1wO+vqWuxCZuTBEnUSzNXHiAx5Tyero7MfbAXTZxtsNfa0TWw8QP1+PaPoGkr7esUQti9LiHevDlanWg0K+4QGw6fhf5PQ9PWkH8GVsuAbT1IgiTqnRW7zvDlH8cBmHVPBGH+NthE+PyJikHZCvScAF3u1L5OIYSocG/vUMb1DUVR4NlFyZwuBG6Zpb649TM4k6xneHZJEiRRrxzJzOeVH3YB8MSQtgzvcuXVz62mpAC+ux8uZENID7j5He3rFEKIS8y4rQtdm3uRXVjKo/O3UxA6BLqOAcUMPz+jjpEUNiMJkqg3CkrKeeLbJApLTfRr48uLwztoX6nZDD8+Dpl7wT0Qxi4Apyba1yuEEJdwdXJg7oO98PdwZl9aHk8vTKL8prfA1QfSdl4cAiBsQhIkUS+YzArPfLeDI5kFBHq68Mm4njg62ODruf5fcGAFODjDfQvA20b7uwkhRDVaNHXjPxP64OJoZM3Bs7yx9hzKzf9SX1z3DqTt0jdAOyIJkqgX3vplP6sPZOLiaOSzh3oR4OmifaX7lsPamerxrR9AaF/t6xRCiGuIDPXhw7GRGAzwTUIKX+b1gfBbwVwOPz4BZcV6h2gXJEESupu/6YRlUPb790bSo6UN9sNL2QRLH1WPo56AHg9qX6cQQtTQzd2aMe3mcAD+sXI/a9v/Fdz81eEAv/+fztHZB0mQhK5W7DrD6z/vBeDF4R24pXsz7StN2wULx0J5MXQYCcNlCq0Qov55dFAbxvVtiaLAE8tS2R/9rvrCtv+oLeBCU5IgCd38cSSL5xcnoyjwUL9WPHVDO+0rPXcUvh2jrpTdsj/c8zU4OGpfrxBC1JLBYOCNO7pwQ8cAisvMjIlzI73r4+qLP01Rf54JzUiCJHSx9Xg2j8zfTplJYVS3YF6/vQsGg0HbSvPS4L+joTATgrrB/YtkxpoQol5zcjDy6YO9GNDOj6JSEzfvHkJhYC8oyYVF98sq2xqSBEnYXGLKeSZ9tZULZSYGtffng7GROBg1To7y09XkKCcVfNvAQ0vB1VvbOoUQwgpcnRz4z/g+RLX25XwJ3Jb5GGVuQXD2ACx9XF2uRFidJEjCprYez2bCl1spLDXRv60fn4/vjYujxtuInE+BL0eqP0w8m8FDP4JHoLZ1CiGEFTVxduDLiX3o1aopx4o9efjCs5iNznDwF/j9b3qH1yhJgiRsZv2hs4z/cgsFJeVEtfblPxN64+qkcXJ09hB8dTOcPw4+rWDSr9A0TNs6hRBCA+4ujnw1qQ8RoT5suBDGNPMT6gub50DCHH2Da4QkQRI28fPOMzwyfzvFZWaGdgzg60l9cXPWeHB02k41Oco7Df4d4eFY8G2tbZ1CCKEhL1cnvnm4L71aNWVxcT/+ZX5AfeG3v0LyQn2Da2QkQRKaUhSFT9ce5envdlBqMjOqWzDzHupNE2eNW44Or4Kvb4OiLGgWqbYceYVoW6cQQtiAdxMnvp0cxdCOAfy7dBRflt+svrDsL7Bzsb7BNSKSIAnNlJSbePV/u3kn9gAADw9ozSfjeuLsqOHXTlHU/YoW3K3O8mjZHyYsB3c/7eoUQggba+LswOfje3N3r1DeKH+Qb8uHAQrKsiekJclKZAEYoYn03GKe+DaR5JM5GA3wt1s68/BAjbu3Sotg+RTY8z/1v3tOgFHvgqMNti0RQggbc3Iw8u7d3WkT4M5rsZNwwMw4xzWw7Ekoyob+U/QOsUGTBElY3ZqDmbz4/U7OFZbi3cSJT8b1YHCHAG0rPXcUlkyA9N1gdISb/wW9Hwat11YSQggdGQwG/jK0HW38PXhpiSP55W485viLuh1J7ikY/g9ZDLeO5K4JqykuM/Hubwf5YqO6r1qnZl589mAvWvq5aVep2QzbPoe4GVB+AdwD4N5voFV/7eoUQoh6ZmTXYNoHDeKJb5pwLtuLaU7fwZZPMWcewHjvV9DEBntcNjIGRVEUvYNoiPLy8vD29iY3NxcvLy+9w9FdYsp5XvphJ8fOFgIwsX8Yr94cru00/pxUdVDiiQ3qf7cZCnf8G7yba1enEELUY0Wl5bzx8z5yEv/H+06f4mYoodSjBc5jv4LQvnqHVy/U9Pe3JEh1JAmSKqeolPd+P8iCLakoCgR6ujDzrm4M6xSkXaWmMtj+JcS/CaX54OQGN70BvSeDUeYdCCFE7J50vvrfct4xvUeYMQMzDpQOegnXoS+Cg5Pe4elKEiSN2XuCVGYys3jbSd6PO0R2YSkAY3q2YPqtnfF20+gfn6LAoVh11dhzR9RzLaPhjjng11abOoUQooE6V1DCOz9tY+CBf3C7QwIA573C8Ro7F4fmPXSOTj+SIGnMXhOkcpOZX3an8eGqwxzPUrvTOgR58MYdXenXRsOp9KeTYNUMOL5e/W/3ALjhr+pMNaPGayoJIUQDtvZABpt+/JS/FM/Dx1CIGQOn2txLi7v+idHDX+/wbE4SJI3ZW4JUWFLO4m0n+WLjcU7nXADAz92ZZ4a15/6oljg5aNC1pShwZBX88dHFcUYOLhD9FAx8Hlwb/30XQghrKC038/2a7fj/MYORbAKgkCYcazeR9qNfwdXDfgZxS4KkMXtJkFLPFbF4eyrfbk4l90IZAL7uzkzsH8bDA1vj4aLBRMiSfNi7DDb/GzL3qeeMjtD1brXVqGkr69cphBB2IPdCGbErfqD7nrfpZDgBQD5uHGp2O61GPod/q076BmgDkiBprDEnSOcLS1mxO41lO06TmHLecj7Mz41HBrXh7l4trD87zWyCY2vUZfL3/6xO2Qdw9oBeE6Hfk+Ddwrp1CiGEncq/UMLWlV/TZs/HtFZOAWBWDCS79iG/2wQih9yFt6eGS7ToSBIkjTWmBElRFI6eLWDdoSzWHzrLH0eyKDerXwujAfq39efBfi25qXMwDkYrLrxYnAtH18DhODj8OxRmXnzNrz30eAB6TYImPtarUwghhIXJZCJp9f9w2D6PniXbLOdzFHf2eA6krOPttIm6hVaBjacLThIkjTXkBMlkVjicmc+uk7kkppxnw+GznMktrlKmS4gXd/Zozm0RIQR5uVqn4qJsOLUNTm6F1AQ4uQXM5Rdfb+ILXcdAxDho3lNWwRZCCBtKO7qbs6tn0/LMr/gouZbzeUoT9jh0JiewL24dhhDaJZrWAd4YrfkHsw01qARpzpw5vPvuu6SnpxMREcEnn3xC375XXtBqyZIlvPbaa5w4cYL27dvzzjvvMGrUKMvriqIwY8YMPv/8c3JychgwYACffvop7du3t5TJzs7m6aef5ueff8ZoNDJmzBg++ugjPDw8ahRzQ0iQFEUhM7+Eo5kFHM0q5GhmAfvO5LH7dC4XykxVyjo7Golq7cvg9gHcEB5Au0DPuldcXgrnj0Pmfjh7QH1O3w3ZRy8v698B2g+H9jepG8s6Ote9XiGEENfPbOJU8irObVtCaEY8vubsKi8XKK4cJIyz7u0xBXTGuXl3fFt1pWVIM/w9nDHU8z9uG0yCtHjxYsaPH8/cuXOJioriww8/ZMmSJRw8eJDAwMDLym/atInBgwczc+ZMbr31VhYuXMg777xDUlISXbt2BeCdd95h5syZzJ8/n9atW/Paa6+xe/du9u3bh6ur2hpy8803k5aWxmeffUZZWRmTJk2iT58+LFxYs12Q9UyQSspN5BSVkVNUxvmiUnKKSjlfVEZmXglpuRdIyy0mLfcCZ3KKKSgpr/YaHi6OdG3uRUSoD/3b+tM3zJcmztcYV1ReAkXnoDALCs+qz0UVx3ln1JWtc1LVY67wtfLvAC36qI82Q8FX4w1shRBC1J3ZzIXUJE4nr8J0YiPNc3fgoRRUW/S84sEpgsh2DuFCk2DM7gE4egbi4tMMV+8gXJsG4940EC8Pd7ybOOHiqM8SLQ0mQYqKiqJPnz7Mnj0bALPZTGhoKE8//TSvvvrqZeXHjh1LYWEhK1assJzr168fkZGRzJ07F0VRCAkJ4YUXXuDFF18EIDc3l6CgIL7++mvuu+8+9u/fT+fOndm2bRu9e/cGIDY2llGjRnHq1ClCQkKuGbdWCdJn646yLy2PolITF0pNFJWWc6HMzIXScopKTRSUqM815WA00NLXjTb+7rQN9KBDkCcRLbxpE+BRdTzR6rcg+xiUFkJpQcVzYdX/NpfV/IM4uUNARwjsDIHhENgJQnqCm28t7oYQQoh6xWyiPH0fmUcSyTuxA8ez+/AvPILPJa1MV1OiOFGAK0U04YLRjWKjGyXGJpQbXTE5uJLpEsa6oIdo4uTAhP5hdGpm3UaImv7+1nWz2tLSUhITE5k2bZrlnNFoJCYmhoSEhGrfk5CQwNSpU6ucGzFiBMuWLQPg+PHjpKenExMTY3nd29ubqKgoEhISuO+++0hISMDHx8eSHAHExMRgNBrZsmULd95552X1lpSUUFJSYvnv3Fy1fzYvL6/2H/wq1uxJYdORc9csZzSAdxMny8PHzQk/dxeCvV0J9nIl2MeVIC9XQpu64ex46RpFCoUF+VVP7VwBmXtrEKEDuPtCEz9w9wM3P3D3B/dA8AkF74qHu//lY4jKASvfLyGEEDbm0QqPyFZ4RN5lOZVXUkDJuRTOnT7EhYzjlOemoRRm4XjhHM6l2XiU5+BNHg6YgVKcKMWbPLyrufw2cwd+PBENwJDWHjR3t274lb+3r9U+pGuClJWVhclkIiio6r5dQUFBHDhwoNr3pKenV1s+PT3d8nrluauVubT7ztHREV9fX0uZS82cOZO///3vl50PDQ290sdrxHKAY3oHIYQQolFKBO4FYNSH2tWSn5+Pt3d1KZpK1wSpIZk2bVqVliuz2Ux2djZ+fn71ekBaXl4eoaGhnDx5st4OJrc1uSdVyf24nNyTquR+XE7uSVUN6X4oikJ+fv41h9PomiD5+/vj4OBARkZGlfMZGRkEBwdX+57g4OCrlq98zsjIoFmzZlXKREZGWspkZmZWuUZ5eTnZ2dlXrNfFxQUXF5cq53x8fK7+AesRLy+vev+ltTW5J1XJ/bic3JOq5H5cTu5JVQ3lflyt5aiSBhto1ZyzszO9evUiPj7ecs5sNhMfH090dHS174mOjq5SHiAuLs5SvnXr1gQHB1cpk5eXx5YtWyxloqOjycnJITEx0VJm9erVmM1moqKirPb5hBBCCNEw6d7FNnXqVCZMmEDv3r3p27cvH374IYWFhUyaNAmA8ePH07x5c2bOnAnAs88+y5AhQ5g1axa33HILixYtYvv27cybNw8Ag8HAc889xz/+8Q/at29vmeYfEhLC6NGjAejUqRMjR47k0UcfZe7cuZSVlTFlyhTuu+++Gs1gE0IIIUTjpnuCNHbsWM6ePcv06dNJT08nMjKS2NhYyyDr1NRUjMaLDV39+/dn4cKF/O1vf+Ovf/0r7du3Z9myZZY1kABefvllCgsLeeyxx8jJyWHgwIHExsZa1kACWLBgAVOmTGHYsGGWhSI//vhj231wG3FxcWHGjBmXdQ/aM7knVcn9uJzck6rkflxO7klVjfF+6L4OkhBCCCFEfaPrGCQhhBBCiPpIEiQhhBBCiEtIgiSEEEIIcQlJkIQQQgghLiEJUgO1fv16brvtNkJCQjAYDJa96KrzxBNPYDAY+PDDD6ucz87O5oEHHsDLywsfHx8mT55MQUH1uzTXd9e6HxMnTsRgMFR5jBw5skqZxnQ/oGbfkf3793P77bfj7e2Nu7s7ffr0ITU11fJ6cXExTz31FH5+fnh4eDBmzJjLFmptKK51Py79flQ+3n33XUsZe/uOFBQUMGXKFFq0aEGTJk3o3Lkzc+fOrVLGnr4jGRkZTJw4kZCQENzc3Bg5ciSHDx+uUqYx3Y+ZM2fSp08fPD09CQwMZPTo0Rw8eLBKmZp83tTUVG655Rbc3NwIDAzkpZdeory83JYfpU4kQWqgCgsLiYiIYM6cOVct9+OPP7J58+Zq13d64IEH2Lt3L3FxcaxYsYL169fz2GOPaRWypmpyP0aOHElaWprl8d1331V5vTHdD7j2PTl69CgDBw4kPDyctWvXsmvXLl577bUqy2E8//zz/PzzzyxZsoR169Zx5swZ7rrrrmqvV99d6378+buRlpbGl19+icFgYMyYMZYy9vYdmTp1KrGxsXz77bfs37+f5557jilTprB8+XJLGXv5jiiKwujRozl27Bg//fQTO3bsoFWrVsTExFBYWGgp15jux7p163jqqafYvHkzcXFxlJWVMXz48Fp9XpPJxC233EJpaSmbNm1i/vz5fP3110yfPl2Pj1Q7imjwAOXHH3+87PypU6eU5s2bK3v27FFatWqlfPDBB5bX9u3bpwDKtm3bLOd+/fVXxWAwKKdPn7ZB1Nqp7n5MmDBBueOOO674nsZ8PxSl+nsyduxY5cEHH7zie3JychQnJydlyZIllnP79+9XACUhIUGrUG3iSv9m/uyOO+5QbrzxRst/2+N3pEuXLsobb7xR5VzPnj2V//u//1MUxb6+IwcPHlQAZc+ePZZzJpNJCQgIUD7//HNFURr3/VAURcnMzFQAZd26dYqi1Ozzrly5UjEajUp6erqlzKeffqp4eXkpJSUltv0AtSQtSI2U2WzmoYce4qWXXqJLly6XvZ6QkICPjw+9e/e2nIuJicFoNLJlyxZbhmoza9euJTAwkI4dO/Lkk09y7tw5y2v2dj/MZjO//PILHTp0YMSIEQQGBhIVFVWlSyExMZGysjJiYmIs58LDw2nZsiUJCQk6RG07GRkZ/PLLL0yePNlyzt6+I6AuzLt8+XJOnz6NoiisWbOGQ4cOMXz4cMC+viMlJSUAVVpYjUYjLi4ubNy4EWj89yM3NxcAX19foGafNyEhgW7dulkWfwYYMWIEeXl57N2714bR154kSI3UO++8g6OjI88880y1r6enpxMYGFjlnKOjI76+vqSnp9siRJsaOXIk33zzDfHx8bzzzjusW7eOm2++GZPJBNjf/cjMzKSgoIC3336bkSNH8vvvv3PnnXdy1113sW7dOkC9J87OzpdtyhwUFNQo78mfzZ8/H09PzypdBfb2HQH45JNP6Ny5My1atMDZ2ZmRI0cyZ84cBg8eDNjXd6TyF/+0adM4f/48paWlvPPOO5w6dYq0tDSgcd8Ps9nMc889x4ABAyw7V9Tk86anp1dJjipfr3ytPtN9qxFhfYmJiXz00UckJSVhMBj0DqdeuO+++yzH3bp1o3v37rRt25a1a9cybNgwHSPTh9lsBuCOO+7g+eefByAyMpJNmzYxd+5chgwZomd4uvvyyy954IEHqrQW2KNPPvmEzZs3s3z5clq1asX69et56qmnCAkJqdJqYA+cnJxYunQpkydPxtfXFwcHB2JiYrj55ptR7GBDiqeeeoo9e/ZYWsvsgbQgNUIbNmwgMzOTli1b4ujoiKOjIykpKbzwwguEhYUBEBwcTGZmZpX3lZeXk52dTXBwsA5R21abNm3w9/fnyJEjgP3dD39/fxwdHencuXOV8506dbLMYgsODqa0tJScnJwqZTIyMhrlPam0YcMGDh48yCOPPFLlvL19Ry5cuMBf//pX3n//fW677Ta6d+/OlClTGDt2LO+99x5gf9+RXr16kZycTE5ODmlpacTGxnLu3DnatGkDNN77MWXKFFasWMGaNWto0aKF5XxNPm9wcPBls9oq/7u+3xNJkBqhhx56iF27dpGcnGx5hISE8NJLL/Hbb78BEB0dTU5ODomJiZb3rV69GrPZTFRUlF6h28ypU6c4d+4czZo1A+zvfjg7O9OnT5/LpuweOnSIVq1aAeovAycnJ+Lj4y2vHzx4kNTUVKKjo20ary198cUX9OrVi4iIiCrn7e07UlZWRllZWZXNwgEcHBwsLZD2+h3x9vYmICCAw4cPs337du644w6g8d0PRVGYMmUKP/74I6tXr6Z169ZVXq/J542Ojmb37t1V/riIi4vDy8vrsj/Q6h2dB4mLOsrPz1d27Nih7NixQwGU999/X9mxY4eSkpJSbflLZ7EpiqKMHDlS6dGjh7JlyxZl48aNSvv27ZVx48bZIHrru9r9yM/PV1588UUlISFBOX78uLJq1SqlZ8+eSvv27ZXi4mLLNRrT/VCUa39Hli5dqjg5OSnz5s1TDh8+rHzyySeKg4ODsmHDBss1nnjiCaVly5bK6tWrle3btyvR0dFKdHS0Xh/putTk30xubq7i5uamfPrpp9Vew96+I0OGDFG6dOmirFmzRjl27Jjy1VdfKa6ursq///1vyzXs6Tvy/fffK2vWrFGOHj2qLFu2TGnVqpVy1113VblGY7ofTz75pOLt7a2sXbtWSUtLszyKioosZa71ecvLy5WuXbsqw4cPV5KTk5XY2FglICBAmTZtmh4fqVYkQWqg1qxZowCXPSZMmFBt+eoSpHPnzinjxo1TPDw8FC8vL2XSpElKfn6+9sFr4Gr3o6ioSBk+fLgSEBCgODk5Ka1atVIeffTRKtNOFaVx3Q9Fqdl35IsvvlDatWunuLq6KhEREcqyZcuqXOPChQvKX/7yF6Vp06aKm5ubcueddyppaWk2/iTWUZP78dlnnylNmjRRcnJyqr2GvX1H0tLSlIkTJyohISGKq6ur0rFjR2XWrFmK2Wy2XMOeviMfffSR0qJFC8XJyUlp2bKl8re//e2yqeqN6X5Udy8A5auvvrKUqcnnPXHihHLzzTcrTZo0Ufz9/ZUXXnhBKSsrs/GnqT2DotjB6DIhhBBCiFqQMUhCCCGEEJeQBEkIIYQQ4hKSIAkhhBBCXEISJCGEEEKIS0iCJIQQQghxCUmQhBBCCCEuIQmSEEIIIcQlJEESQgghhLiEJEhCCCGEEJdw1DsAIYTQw7p163j88cdxdXWtct5sNjNkyBC2bt1KSUnJZe8rKChg7969uLi42CpUIYQOJEESQtilCxcucN999/H6669XOX/ixAleffVVDAYDycnJl71v6NChyA5NQjR+0sUmhBBCCHEJSZCEEEIIIS4hCZIQQgghxCUkQRJCCCGEuIQkSEIIIYQQl5AESQghhBDiEpIgCSGEEEJcQhIkIYQQQohLSIIkhBBCCHEJSZCEEEIIIS4hW40IIeySt7c3K1asYMWKFZe9NmLECHJycujdu3e17zUa5W9LIRo7gyKbCgkhhBBCVCF/BgkhhBBCXEISJCGEEEKIS0iCJIQQQghxCUmQhBBCCCEuIQmSEEIIIcQlJEESQgghhLiEJEhCCCGEEJeQBEkIIYQQ4hL/D7U2N0dBygIDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(region_a_height2)\n",
    "sns.kdeplot(region_b_height2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.9906963757270788 0.04651427741343414\n",
      "两组数据有显著差异\n"
     ]
    }
   ],
   "source": [
    "# 计算z值和p值（双尾）\n",
    "z_stat, p_value = ztest(region_a_height2, region_b_height2, alternative='two-sided')\n",
    "print(z_stat, p_value)\n",
    "\n",
    "# 显著水平为0.05（显著水平一般用alpha字母表示）\n",
    "alpha = 0.5\n",
    "\n",
    "# 比较计算出的p值和显著水平，打印是否有显著差异的结论\n",
    "if p_value < alpha:\n",
    "    print('两组数据有显著差异')\n",
    "else:\n",
    "    print('两组数据无显著差异')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.9906963757270788 0.9767428612932829\n",
      "两组数据无显著差异\n"
     ]
    }
   ],
   "source": [
    "# 计算z值和p值（单尾正差异）\n",
    "z_stat, p_value = ztest(region_a_height2, region_b_height2, alternative='larger')\n",
    "print(z_stat, p_value)\n",
    "\n",
    "# 显著水平为0.025（显著水平一般用alpha字母表示）\n",
    "alpha = 0.025\n",
    "\n",
    "# 比较计算出的p值和显著水平，打印是否有显著差异的结论\n",
    "if p_value < alpha:\n",
    "    print('两组数据有显著差异')\n",
    "else:\n",
    "    print('两组数据无显著差异')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.9906963757270788 0.02325713870671707\n",
      "两组数据有显著差异\n"
     ]
    }
   ],
   "source": [
    "# 计算z值和p值（单尾负差异）\n",
    "z_stat, p_value = ztest(region_a_height2, region_b_height2, alternative='smaller')\n",
    "print(z_stat, p_value)\n",
    "\n",
    "\n",
    "# 显著水平为0.025（显著水平一般用alpha字母表示）\n",
    "alpha = 0.025\n",
    "\n",
    "# 比较计算出的p值和显著水平，打印是否有显著差异的结论\n",
    "if p_value < alpha:\n",
    "    print('两组数据有显著差异')\n",
    "else:\n",
    "    print('两组数据无显著差异')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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